Machine Learning in Fixed Income Markets

收益率曲线是债券市场的核心，债券市场是一个庞大的资产类别，总规模为100万亿美元，使用机器学习仍然相对不足。该项目的目标是通过为金融业开发机器学习模型和技术的创新应用，将现有的机器学习模型扩展到固定收益资产类别，从而产生更好的预测系统。总体而言，它包括评估和调整现有技术，以建模和预测收益率曲线(即利率作为到期时间的函数)，并评估与单任务学习相比，多任务学习(即同时对几个因变量进行建模)的潜在好处，这是由于将问题转化为独立的单目标过程而产生的。这项研究的主要社会价值与债券在养老基金和保险投资组合中的重要性直接相关。但社会和行业的几个部门可以直接从这项研究中受益，包括央行的政策制定、资产和风险管理行业以及学术界。

The yield curve is the centrepiece in bond markets, a massive asset class with an overall size of USD 100 trillion that remains relatively under-investigated using machine learning. The objective of this project is to extend existing machine learning models to the fixed income asset class, by developing innovative applications of machine learning models and techniques for the financial industry, leading to better forecasting systems. Overall, it includes assessing and adapting existing techniques to model and forecast the yield curve (i.e. interest rates as a function of time to maturity) and evaluate potential benefits of multitask learning (i.e. simultaneous modelling of several dependent variables) when compared to single task learning, resulting from transformation of the problem into independent single-target processes. The main societal value of this research is directly related to the importance of bonds in pension fund and insurance portfolio holdings. But several sectors of society and industry could benefit directly from this research, including central banks' policy making, the asset and risk management industry, and academia.